ESM-NBR
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Hunan University
- Organisation type
- Academia
- Country
- China
- Published
- 18 January 2024
- Authors
- Wenwu Zeng, Dafeng Lv, Xuan Liu, Guo Chen, Wenjuan Liu, Shaoliang Peng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein nucleotide interaction prediction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Summary for ESM-NBR data estimate: Pre-training (UniRef50): 43,000,000 proteins × 300 residues = 12,900,000,000 tokens (1.29e10) Training (YK17-Tr + DRNATr-1068): 2,068 proteins × 300 residues = 620,400 tokens (6.2e5) Total: 12,900,000,000 + 620,400 ≈ 1.29e10 tokens Final estimate: 1.3e10 data points
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 7
Sources
Where this record came from and when it was last checked.
- Reference
- ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ESM-NBR was published by Hunan University, in China, in January 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein nucleotide interaction prediction.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
ESM-NBR — common questions
When was ESM-NBR released?
ESM-NBR was published in January 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is ESM-NBR used for?
ESM-NBR works in Biology, and is recorded as handling protein nucleotide interaction prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run ESM-NBR?
None. ESM-NBR is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is ESM-NBR open source?
The licensing for ESM-NBR was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ESM-NBR have?
No parameter count has been published for ESM-NBR, which is why no memory or speed figure appears on this page.
Who created ESM-NBR?
ESM-NBR was published by Hunan University, based in China, categorised as academia.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.